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LSMF for Suppressing Multiples

LSMF for Suppressing Multiples. Jianhua Yu. University of Utah. Contents. Motivation. LSMF Inversion. Numerical Examples. Conclusions. Contents. Motivation. LSMF Inversion. Numerical Examples:. Conclusions. Demultiple Methods. Radon transform. Inverse scattering theory.

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LSMF for Suppressing Multiples

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  1. LSMF for Suppressing Multiples Jianhua Yu University of Utah

  2. Contents Motivation LSMF Inversion Numerical Examples Conclusions

  3. Contents Motivation LSMF Inversion Numerical Examples: Conclusions

  4. Demultiple Methods Radon transform Inverse scattering theory Prediction+subtraction

  5. Benefits: Demultiple for coarse acquisition geometry Use both primary and multiple information

  6. Contents Motivation LSMF Inversion Numerical Examples: Conclusions

  7. D : seismic data p m p m L : Primary forward operator L : Multiple forward operator R : Primary model R : Multiple model Assuming that seismic data can be written mathematically as

  8. obs D : seismic data p m R : Primary model R : Multiple model LSMF equation (Nemeth, 1996) : Minimize the misfit function

  9. LSMF Inversion: Algorithm: Conjugate Gradient (CG)

  10. p W a weight p R A primary model p d primary reflections Primary Modeling Operator

  11. m W a weight m R A multiple model m d multiples reflections Multiple Modeling Operator

  12. Multiple initial model Wang (Geophys, 2003) Operators for primary and multiple migration are the transpose of modeling operators

  13. Solving the following equation by CG algorithm Demultiple Using LSMF Input CMP gathers

  14. Subtract multipleM from raw dataDand get primaryP P=D-M Demultiple using LSMF Predicted multipleM

  15. Contents Motivation LSMF Inversion Numerical Examples Conclusions

  16. Model Time (s) P+M P M LSMF P+M P M

  17. CMP 300 (NS) Time (s) P+M M P

  18. CMP 1700 (NS) Time (s) P+M M P

  19. CMP 1300(NS) Time (s) P+M M P

  20. Velocity Velocity 1.4 4 Time (s) 3.5 Before LSMF CMP 1300 (NS) After LSMF 0

  21. Velocity Velocity 1.4 4 Time (s) 3.5 Before LSMF CMP 1300 (NS) After LSMF 0

  22. CMP 800 (Unocal) Time (s) P+M M P

  23. CMP 900 (Unocal) Time (s) P+M M P

  24. CMP 1100 (Unocal) Time (s) P+M M P

  25. Velocity Velocity 1.4 3.2 1.4 3.2 0 Time (s) 4 After LSMF Before LSMF

  26. Contents Motivation LSMF Inversion Numerical Examples: Conclusions

  27. Works for synthetic data and real data No limit to coarse geometry Straightforward to extend to 3D Regularization strategy required for shallow reflections 1-D model Conclusions

  28. Unocal and Mobil for 2-D field data ACKNOWLEDGMENTS 2003 UTAM Sponsors CHPC

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